Artificial Intelligence in Medicine: A Cross-sectional Study of Knowledge and Attitudes

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However, there are obstacles impeding the proper integration of AI into the medical field. This study was undertakento evaluate the knowledge, attitude, and awareness of medical students and resident doctors regarding AI in medicine and healthcare. Methods: A questionnaire-based survey was conducted that included a total of 16 questions specifically designed to assess the knowledge and attitude of participants towards AI. The questionnaire used in the present study was developed for this study only and content validity of the initial questionnaire was adequately assessed. The questionnaire was converted into a Google Form, and participants were provided with the link to complete it. Statistical analysis was conducted using R version 4.3.2 (R-Studio). Results: Out of 194 respondents, 113 (58.25%) were medical students, and 81 (41.75%) were resident postgraduate doctors aged 19 to 32 (average 23.91 years) and a male-to-female ratio of 3.62:1. While 63.41% rated their AI knowledge as poor to below average, with 55.15% lacking understanding of many AI terminologies, 59.28% believed AI tools could enhance their understanding of medical concepts. 83.5% expressed interest in furthering knowledge on AI in healthcare. ChatGPT was the most used AI tool, primarily for language correction (50%), literature reviews and manuscript writing (43.3%), and creating presentation outlines (37.11%). Additionally, knowledge about AI devices and apps applicable to diagnostics, therapeutics, patient care, and data analysis was evaluated, along with opinions on barriers to incorporating AI in healthcare. 81.44% of respondents were unaware of AI's ethical considerations. Conclusions: AI has immense potential across diverse healthcare sectors. Nonetheless, our study also underscores the pressing need to confront challenges and equip our future healthcare professionals with the evolving realm of AI. This is essential to ensure they can effectively apply practical AI knowledge for enhanced patient care and management. Artificial intelligence Attitude ChatGPT Knowledge Machine learning Medical curriculum Medical students Resident doctors Figures Figure 1 Figure 2 Figure 3 Background Artificial Intelligence (AI) refers to the ability of machines to reproduce human behaviour like problem-solving ability and reasoning. AI involves the integration of machine learning and deep learning and develops tools to perform tasks that need human-like cognitive abilities. These include, but are not limited to, audio or speech recognition, visual perception, task scheduling and decision-making. 1 Recently, AI has emerged as a transformative force, which could reshape the realm of healthcare by revolutionising medical education, patient care and translational research. 2 , 3 AI tools have been developed in healthcare that have the capability to enhance diagnostic precision, decision-making, prognostication, and management of patients. AI has been integrated into routine healthcare for patients’ recordkeeping, drug discovery, therapy selection, surgical planning and surgical support, such as in robotic surgery. AI has already become an integral part of radiodiagnosis, reinforcing diagnostic accuracy and streamlining workflow. 4 – 7 Despite the previous publications, the optimal use of AI in medicine is not well known. Also, healthcare workers are in general anxious and have concerns about the effectiveness of AI, mainly because of a lack of awareness, knowledge, and education among medical practitioners. To overcome this, the medical curriculum requires an overhaul which would improve the willingness to use AI effectively in future. The current study aimed to assess the awareness, perceptions, and opinions regarding AI among medical students and resident doctors at a tertiary care teaching hospital in North India. Methods Study design, Setting, and Population We conducted a questionnaire-based, cross-sectional survey among medical students and resident doctors at a tertiary care teaching hospital in North India, from 20.3.25 to 29.4.25. All MBBS students from the first year to the final year were invited to participate along with all resident doctors. The study protocol was ethically approved by the Institutional Ethics Committee [AIIMSA3369/11.03.2025]. Participation in this study was voluntary and not compensated. Electronic informed consent was obtained from all the participants. Medical students and resident doctors who did not give consent were excluded from the study. Development and administration of the questionnaire The first section of the questionnaire collected demographic information of the participants, including their age, gender, year of study, and department (for postgraduate students). Subsequent sections collected data on their knowledge and attitudes towards AI for which a total of sixteen questions were used to assess. The questionnaire used in the present study was developed for this study only and content validity of the questionnaire was adequately assessed (Supplementary PDF file attached of the questionnaire used in the present study). All the participants were informed about the confidential nature of the collected data and its use for the purposes of this study only. The initial questionnaire underwent content validation by an independent consultant, who also assessed the average response time and consistency of the questionnaire. Subsequently, the questionnaire was transformed into an online questionnaire (Google Forms, Google Inc.), and participants were provided with the link to respond. Statistical analysis R version 4.3.2 (R Studio) was used for statistical analysis. Descriptive statistics were used to represent proportions, means, and standard deviations (SD), or medians and interquartile ranges (IQR). For between-group comparisons of continuous or ordinal variables, the Kruskal-Wallis rank sum test and two-way ANOVA were used. For comparisons of categorical variables, Pearson’s Chi-square test was used. Results A total of 194 participants successfully submitted the questionnaire, including 113 (58.25%) medical students and 81 (41.75%) resident postgraduate doctors. Demographics : Among the 194 participants, the ages ranged from 19 to 32 years, with a mean age of 23.91 years. The study participants were predominantly male (78.35%) with a male-to-female ratio of 3.62:1. Table 1 depicts the baseline characteristics of the study participants. Table 1 Baseline characteristics of the study participants [n = 194] Variables Total [N = 194] Medical students [n = 113] Resident doctors [N = 81] Age range in years (Mean age) 19 to 32 (23.91) 19 to 23 (21.29) 24 to 32 (26.53) Gender, n (%) Female 42 (21.65%) 22 (19.47%) 20 (24.70%) Male 152 (78.35%) 91 (80.53%) 61 (75.30%) 63.41% of the participants declared their knowledge to be poor to below average, and another 24.74% said that they had average knowledge about AI. Only 11.85% of the study participants said that they had above-average or excellent knowledge of AI (Table 2 ). This distribution did not vary significantly when compared amongst medical students and postgraduate resident doctors (Fig. 1 ). 115 (59.28%) participants felt that AI tools will be effective in making their understanding of medical concepts. Similar trends were seen when the answers of medical students and resident doctors were compared (Table 2 ; Fig. 2 ). Table 2 Depicts how the participants (Medical students and Resident doctors] rated themselves for their knowledge and effectiveness of AI tools used in healthcare S No Questions asked on Artificial intelligence ‘Poor’ n (%age) ‘Below average’ n (%age) ‘Average’ n (%age) ‘Above average’ n (%age) ‘Excellent’ n (%age) 1 How is your knowledge about AI/ machine learning algorithms? [Total respondents = 194] 73 (37.63) 50 (25.77) 48 (24.74) 21 (10.82) 2 (1.03) 2 How will you rate the effectiveness of AI tools in enhancing your understanding of medical concepts? [Total respondents = 115] 13 (11.3) 13 (11.3) 47 (40.87) 34 (29.57) 8 (6.96) When asked about the exposure of AI in their medical curriculum, 84.62% of participants denied the exposure of any topics related to AI in their medical curriculum, and another 8.66% of participants responded as ‘not sure’. Only 6.71% of participants responded as ‘yes’ for the exposure of AI in their medical curriculum. Similar results were observed when they were asked about whether AI is being used as a teaching method in the medical curriculum, with 85.05% of participants responding as ‘no’, 11.85% as ‘not sure’ and 3.09% as ‘yes’ (Table 3 ). When asked questions like, “Are you familiar with the ethical considerations associated with the use of AI in healthcare, particularly concerning patient privacy and data security?”- the response of “no/not sure” was given by 81.44% of participants, and “Would you be interested in further education and training programs focused on AI in healthcare?” - the response as “yes” was given by 83.5% of participants, with the responses of medical students as well as resident doctors being similar (Table 3 ). Figure 3 compiles the various AI tools used by medical students and resident doctors, and it was seen that ChatGPT was the most frequently used AI tool amongst all the participants. Table 3 Descriptive statistics for knowledge and attitudes towards artificial intelligence S No Questions asked on Artificial intelligence Participants (n = 194) ‘No’ n (%age) ‘Not sure’ n (%age) ‘Yes’ n (%age) 1 Have you been introduced to AI topics in your medical curriculum? Medical students 95 (84.07) 14 (12.39) 4 (3.54) Resident doctors 69 (85.18) 4 (4.94) 8 (9.88) 2 Is AI being used as a teaching method in your curriculum? Medical students 95 (84.07) 14 (12.39) 4 (3.54) Resident doctors 70 (86.42) 9 (11.11) 2 (2.47) 3 Are you familiar with the ethical considerations associated with the use of AI in healthcare, particularly concerning patient privacy and data security? Medical students 19 (16.81) 75 (66.37) 19 (16.81) Resident doctors 6 (7.4) 58 (71.6) 17 (20.99) 4 Would you be interested in further education and training programs focused on AI in healthcare? Medical students 6 (5.3) 9 (7.97) 98 (86.72) Resident doctors 9 (11.11) 8 (9.88) 64 (79.01) Participants primarily used AI tools for tasks like correcting language (97/194; 50%), conducting literature reviews, statistical analysis and manuscript writing (84/194; 43.3%), creating presentation outlines (72/194; 37.11%), taking notes from detailed content (53/194; 27.32%), and aiding in documentation like converting image or spoken words into text (42/194; 21.65%). Fewer participants engaged in miscellaneous activities like accessing classified documents, coding in Python, general information, writing letters to faculty, drug interactions and making multiple-choice questions (MCQs). When they were asked about their understanding of AI, 107/194; 55.15% of participants were unfamiliar with AI terminologies, while the remaining participants showed awareness of concepts such as machine learning (67/194; 34.54%), neural networks (39/194; 20.1%), generative versus predictive AI (35/194; 18.04%), augmented intelligence (32/194; 16.49%), large language models [LLM] (32/194; 16.49%), and natural language processing (23/194; 11.85%). When enquired about the awareness of the role of AI in diagnostics, 71/194; 36.6% denied any kind of awareness, while 112/194; 57.73% of participants knew that AI can provide differential diagnoses by integrating multiple datasets. Other less frequent roles mentioned included AI tools for analysing chest X-rays (66; 34%), for classification of skin lesions (57; 29.38%), for histopathological assessment (49; 25.26%), for identifying diabetic retinopathy (28; 14.43%) and for the detection of stroke (14; 7.22%) and heart failure (12; 6.18%). Similarly, when enquired about the knowledge of AI application in therapeutics, 97/194; 50% of participants denied any knowledge, while 69; 35.57% of participants said that AI can provide more personalised treatment by integrating multiple data points. Others mentioned that AI tools are used to increase drug adherence (42; 21.65%), AI is used in targeted drug development (39; 20.1%), AI is being used in CRISPR technology (36; 18.56%), and AI tools are used in the NHS to reduce waiting time by segmentation in cancer patients’ radiotherapy (12; 6.18%). Regarding awareness of mobile applications used in the patient care, 53/194; 27.32% denied any kind of awareness, however majority of the participants (118; 60.82%) were aware of IOS Health or Samsung Health, which can store health information, offer medication, track calories, sleep duration and integrate data from various devices. 97/194; 50% of participants were also aware of these mobile applications used by diabetics in order to maintain a glucose log track, carbohydrate intake and exercise, send insulin reminders, facilitate communication with doctors and determine insulin doses, etc. 20; 10.31% of participants were aware of virtual health assistants or apps, and 14; 7.22% of participants were aware of clinical decision support systems. In terms of awareness of AI-enabled devices, 36/194, or 18.56%, of participants were unaware of any such devices. Around 148; 76.29% were familiar with smartwatches like the Apple Watch, which are capable of tracking physical activities, monitoring workouts, measuring heart rate, conducting ECGs and detecting blood oxygen fall, etc. 104/194; 53.61% of the participants were aware of the X3M Littman CORE Digital Stethoscope, which can be used to analyse murmurs. Only 28; 14.43% and 17/194; 8.76% were aware of AI-assisted ultrasound tools that can be used to enhance the accuracy of point-of-care ultrasound and AI-enabled mechanical ventilators for automated weaning, respectively. Lastly, knowledge of sensors for remote monitoring of sleep, breathing, and behaviour was present in 14; 7.22% of the participants. Regarding ethical considerations in the use of AI in medical practices, 158/194; 81.44% showed unawareness of such issues. When asked about the strategies to improve the handling of patients’ data, 90 participants (46.39%) emphasized anonymizing data sharing for research, and 99 participants (51.03%) highlighted the importance of obtaining informed consent from patients for the utilization of their data in AI applications. Another 76; 39.18% of participants advised for implementing regular audits and real-time monitoring systems to prevent unauthorized access, 68; 35.05% supported the development of algorithms with transparent decision-making processes, and 58; 29.9% emphasized collecting and storing only essential patient data for AI purposes. Participants were asked about their views on methods to motivate medical professionals to engage in AI-driven healthcare advancements. The majority (158/194; 81.44%) advocated for familiarizing medical professionals with AI technologies and highlighting their benefits in enhancing patient care, reducing workload, and improving efficiency. Additionally, 132; 68.04% suggested collaboration with other national institutions such as the Indian Institute of Technology (IIT), while 76; 39.18% recommended incentives and recognition. Another 73; 37.63% proposed facilitating networking, and 66; 34.02% stressed addressing privacy and security concerns. Conversely, 17; 8.76% indicated having no opinion on the matter. Participants also opined about various barriers in incorporating AI in healthcare, including data privacy and security concerns (106; 54.64%), doubts about reliability and accuracy of AI algorithms (109; 56.19%), limited AI expertise (125; 64.43%), financial constraints (55; 28.35%), lack of standardized protocols for AI applications (111; 57.22%), resistance to change among professionals (94; 48.45%), strict healthcare industry regulations (54; 27.84%), and challenges in decision-making authority alignment (1; 0.52%). Additionally, inadequate data storage systems pose a hurdle to accurate AI utilization (1; 0.52%). These hurdles highlight the multifaceted challenges facing the integration of AI in healthcare. Discussion The present study was a survey-based cross-sectional study of 113 medical students and 81 resident doctors from a tertiary healthcare centre in North India. The majority of the medical students and resident doctors reported low-level knowledge and limited understanding of AI, underscoring a dire need to learn about the various applications of AI in medical practice and its legal and ethical implications, especially in the present era, marked by the ever-expanding role of AI in medical practice. We did not find significant differences between medical students and resident doctors in their perception about the utility of various AI tools, and this finding was consistent with other previous reports that trainees in the medical field had little exposure to AI in their medical curriculum. A recent web-based survey compared AI-related attitudes amongst 328 medical students and 66 pathology trainees and highlighted that both the groups had similar attitudes about AI knowledge and the majority learnt about the applications of AI through various websites, and this was not part of their medical curriculum. 8 Another recent questionnaire-based cross-sectional study conducted in Riyadh, Saudi Arabia, included 58 resident doctors, 150 medical students and 166 interns. Most of the participants in this study reported having knowledge of AI and about half were aware of its applications in medical practice as well as various AI subtypes such as deep learning and machine learning. About 40% of respondents were taught AI in their medical school, and nearly three-fourths of all participants expressed a strong need for the inclusion of AI in the medical curriculum. 9 The present study and the previous studies suggest a significant knowledge gap in the familiarity and accessibility of structured and well-defined AI curriculum frameworks designed specifically for medical education and training. 10 Although the medical students and practitioners are aware of AI’s potential for early diagnosis and disease management, their concerns regarding ethical and legal implications, the possibility of potential errors and fears of being replaced by AI are valid. Wang F et al. provide a comprehensive review of AI applications in healthcare along with challenges and practical implications of AI in using multiple datasets, research and developmental data and behavioural and wellness data. 11 Overall, there was a strong intent to have quality education and sound knowledge of AI amongst medical students and resident doctors. An Indian study from Kanpur, published in the year 2021, collected responses from 401 medical students across two medical colleges. More than 80% of the participants believed that education in AI would be beneficial for their future medical practice and agreed that it should be included in the medical curriculum. 12 This echoes with another Iranian report where over 96% of medical students demanded enhanced knowledge and skills in AI, indicating that trainees across regions recognize the relevance of AI and support its formal integration into medical education. 13 The present study is in sync with the previously published studies highlighting a lack of adequate knowledge about AI tools in medical practice and a strong urge to learn this technology amongst doctors and medical students. Medical students and doctors have reported use of AI tools, with ChatGPT being the most frequently used AI tool, especially for language and grammatical checks, literature reviews, statistical analysis, manuscript writing, creating presentation outlines and preparing notes. Few participants also used AI for accessing classified documents, coding in Python, drug interactions and preparing MCQs. A U.S.-based survey also highlighted similar findings, indicating that ChatGPT was used by doctors to write, revise and summarize text. 14 Additionally, other systematic reviews also highlighted the utility of ChatGPT in literature searches, drafting reports, and preparing concise summaries. 15 Large language models (LLMs) have been used by clinicians for a variety of tasks such as translating and correcting medical text, drafting patient instructions, assisting in diagnostic report generation, and understanding the interpretation of complex differential diagnoses in challenging clinical scenarios. 15 We, in the present study, also had similar findings, which strongly implicate that the application of AI tools is already being done informally in medical practice. Awareness about AI-related terminologies and applications of AI in diagnostics and therapeutics, along with mobile applications and AI-enabled devices used in medical practice, remains inconsistent amongst medical students and resident doctors. In the present study, although a subset of respondents demonstrated familiarity with terms such as ‘neural network’, ‘augmented intelligence’ and ‘machine learning’, a significant proportion lacked foundational understanding of basic AI concepts. Similar disparities were highlighted in previous studies, where, despite growing interest in AI, familiarity with AI subtypes and operational mechanisms was limited. 7 , 16 AI-assisted diagnostic tools are more frequently being used in the fields of pathology, dermatology and radiodiagnosis, with medical practitioners having limited awareness regarding specific applications such as histopathological classification and staging and automated image interpretation. 17 , 18 Although participants reported using mobile health applications (e.g., glucose monitoring apps, fitness trackers, etc.), fewer were aware of advanced AI-enabled devices like digital stethoscopes, point-of-care ultrasound systems, automated ventilators, CRISPR technology, sleep monitors, etc. This heterogeneity in knowledge indicates a lack of standardized exposure and underscores the need for structured, curriculum-based education to ensure uniform competency in emerging AI technologies across all levels of medical training. Our participants had ethical and practical concerns, especially regarding data privacy, AI related biases and potential reductions in clinical skills due to the easy availability of AI tools and services. 9 , 14 A previous survey from Riyadh, Saudi Arabia, found that over three-quarters of medical trainees expressed concerns regarding the potential impact of AI on job security. 9 Additionally, previous studies have shown a declining interest in pursuing radiodiagnosis as a speciality, likely influenced by the rapid transformation in this field due to increasing integration of AI. 7 In our survey also, respondents have advocated for strict guidelines and education on AI’s limitations and ethical/legal implications, which is in line with global calls for “AI in the loop” oversight. 8 , 14 Some actionable improvements have also been suggested, such as case discussion covering AI’s limitations and drawbacks. 8 , 12 For instance, in a study from Kanpur, India, medical students emphasized that the medical curriculum should cover applications, strengths and limitations of AI and participants of the present study also echoed this by raising a need for training in ethical and legal aspects of AI and various regulations related to it. 12 Our findings accord with the broader English literature. Recent surveys worldwide report similarly low AI literacy coupled with positive attitudes toward its potential and a strong urge to learn its applications and limitations. Another 26-question survey with 702 medical students as respondents from Pakistan highlighted that medical students viewed AI as an effective learning aid and endorsed its formal integration into medical education, as it was better than traditional tools such as books and lectures. 19 Another Indian study highlighted the importance of incorporating AI-related coursework into medical curricula to prepare future healthcare professionals. 20 Previous study from Portugal and a study by Civaner et al. evaluated the integration of AI into medical education and highlighted the importance of incorporating AI-related competencies into medical curricula. 21 , 22 In studies from Riyadh, Saudi Arabia, and Iran, medical trainees recognized the importance of AI in diagnosis yet identified a stark lack of formal training. 9 , 13 Previous reviews also have noted that to date no medical schools mandate AI courses, though some institutions (e.g., Duke University) have begun offering AI modules. 10 These parallels underscore a global consensus: while young clinicians appreciate AI’s promise, medical education has not yet caught up with technological advances. 10 , 19 Our observations in North India fit this pattern. The results of the present study are in sync with previously published research which highlights a strong interest amongst medical trainees to learn about AI tools, coupled with mention of knowledge gaps highlighting an urgent need for structured AI education. Studies have recommended the introduction of AI literacy, including foundational concepts in data science and machine learning, during the early years of medical training. 10 Pilot studies have been conducted for effective implementation of practical courses with hands-on projects to link AI tools and concepts with clinical scenarios. 13 Approximately 96% of medical students and resident doctors in our study expressed a desire for formal training in AI, further reinforcing the necessity of incorporating AI education in medical curricula and training. The present study has a few limitations and drawbacks. Data was collected using a self-report questionnaire at a single teaching hospital, so responses may reflect local context and personal perceptions rather than objective skill. As in other surveys, the convenience sample and survey-based design could introduce response bias. We did not assess actual AI knowledge or performance (only participants’ confidence and attitudes), nor did we measure prior exposure to AI or language proficiency, which may affect familiarity. These factors limit generalizability beyond similar settings. Nonetheless, the consistency of our findings with those from diverse regions suggests these patterns - low AI literacy but high interest - are broadly representative of current medical training environments. Conclusions The present study, including medical students and resident doctors from a tertiary healthcare centre, reveals a striking dichotomy with limited knowledge of AI despite enthusiastic demand for education in AI. Both students and residents recognize AI’s growing role yet report minimal exposure in their medical curriculum. The participants commonly used AI tools for writing and information retrieval but also expressed valid ethical and legal concerns. These insights align with global reports and reinforce the call for curricula reform. Our medical curriculum should incorporate formal AI training, covering technical fundamentals, clinical applications, and ethical implications in a structured way in order to prepare clinicians for an AI-enriched future. Such initiatives will ensure that emerging physicians are equipped to harness AI safely and effectively in patient care. Abbreviations AI Artificial intelligence CRISPR Clustered regularly interspaced short palindromic repeats IQR Interquartile ranges IIT Indian Institute of Technology LLM Large language models MCQS Multiple-choice questions SD Standard deviations Declarations Ethics approval: The study protocol was ethically approved by the Institutional Ethics Committee [AIIMSA3369/11.03.2025] by Institute Ethics Committee, AIIMS, New Delhi. As the present research study was carried out on humans and/or human data, we adhered to the Declaration of Helsinki in our study. Consent to participate and Publish: Consent to participate and publish was obtained from every participant. Competing interest’s statement: Nothing to disclose Financial and non-financial competing interests and Funding: Nothing to disclose Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Clinical Trial Number: Not applicable Author Contributions IG: Conceptualization, Data acquisition, Analysis and interpretation, Literature review, Writing the initial draft and revision, Approval of the final version of the manuscript AG: Data analysis, Writing the initial draft and revision, Literature review, Approval of the final version of the manuscript AV: Formal analysis, Review, Editing, Approval of the final version of the manuscript SJ: Data interpretation, Supervision, Clinical patient management, Manuscript editing, Approval of the final version of the manuscript NW: Data interpretation, Supervision, Clinical patient management, Manuscript editing, Approval of the final version of the manuscript Acknowledgements: Nil Authors' information (optional): NA References Aronson JK. Artificial intelligence in pharmacovigilance: An introduction to terms, concepts, applications, and limitations. Drug Saf. 2022;45:407–18. 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PLoS ONE. 2023;18:e0290613. Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ. 2022;22:772. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Jan, 2026 Reviews received at journal 30 Dec, 2025 Reviewers agreed at journal 21 Dec, 2025 Reviews received at journal 18 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviews received at journal 05 Aug, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviewers invited by journal 15 Jul, 2025 Editor assigned by journal 15 Jul, 2025 Submission checks completed at journal 14 Jul, 2025 First submitted to journal 14 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7059359","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487041380,"identity":"6e2c470e-6cda-4c79-81ff-444c8a8a57eb","order_by":0,"name":"Ishan Gupta","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ishan","middleName":"","lastName":"Gupta","suffix":""},{"id":487041381,"identity":"86e4f31c-14b5-4f11-a53a-856c7f11502c","order_by":1,"name":"Ankush Garg","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ankush","middleName":"","lastName":"Garg","suffix":""},{"id":487041384,"identity":"7e795d3c-2ffa-401c-aa2a-2fd3410eeac2","order_by":2,"name":"Ashwin Varadarajan","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ashwin","middleName":"","lastName":"Varadarajan","suffix":""},{"id":487041385,"identity":"77962765-6e6e-4d50-b666-56736b2e2bdf","order_by":3,"name":"Siddharth Jain","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYDACZsYGEMXDcCAHRB+QA5MPiNeScMAYrCWBKOugWhLBJuDTwt/O3LrxB0OdDN/x3IOfK3/cSZ8fdvgh0BY7Od0G7FokDjO23eZhOMwjeeZdsuSZhGe5G2+nGQC1JBubHcBhDUgL0FU8BjdyDCQbEg7nbpydANJyIHEbDi3yQC03gQ4DaTH+CdSSbjg7/QNeLQZALTd4GJhBWsxAtiTIS+fgt8UQ7BcDkF/emFk2pB023CCdU3AgwQC3X+TOH39280dFnT3f8Rzjmw02h+XlZ6dv/vChwk4Op/chzkNmH0AXIQjkG0hRPQpGwSgYBSMBAACzvWjHVvGoPwAAAABJRU5ErkJggg==","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Siddharth","middleName":"","lastName":"Jain","suffix":""},{"id":487041386,"identity":"71bf7deb-2c09-4808-87a8-95e4e95e0e2c","order_by":4,"name":"Naveet Wig","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Naveet","middleName":"","lastName":"Wig","suffix":""}],"badges":[],"createdAt":"2025-07-06 17:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7059359/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7059359/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87359597,"identity":"b029eb25-6fe0-4a4a-a62f-4704a635d242","added_by":"auto","created_at":"2025-07-23 05:40:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":336242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerception regarding the effectiveness of AI tools in enhancing understanding of medical concepts amongst A) medical students and B) resident doctors\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7059359/v1/2751409d27748f3b1e034cfd.jpeg"},{"id":87360657,"identity":"22a13d26-7a17-43ba-a08e-99bc79293201","added_by":"auto","created_at":"2025-07-23 05:48:19","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerception regarding the effectiveness of AI tools in enhancing understanding of medical concepts amongst A) medical students and B) resident doctors\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7059359/v1/aef98dab09005d110eb48582.jpeg"},{"id":87359606,"identity":"1c69a9b3-b622-47d2-8964-44c09377b605","added_by":"auto","created_at":"2025-07-23 05:40:19","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116809,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVarious AI tools used by medical students and resident doctors\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7059359/v1/f5ae3112e834000cb748f9e0.jpeg"},{"id":87363311,"identity":"5ebefc87-acf3-48a7-b93f-31956017d2f6","added_by":"auto","created_at":"2025-07-23 06:04:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1451566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7059359/v1/8b813297-43db-4aa4-a23e-f6e6368f89d1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Artificial Intelligence in Medicine: A Cross-sectional Study of Knowledge and Attitudes","fulltext":[{"header":"Background","content":"\u003cp\u003eArtificial Intelligence (AI) refers to the ability of machines to reproduce human behaviour like problem-solving ability and reasoning. AI involves the integration of machine learning and deep learning and develops tools to perform tasks that need human-like cognitive abilities. These include, but are not limited to, audio or speech recognition, visual perception, task scheduling and decision-making.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Recently, AI has emerged as a transformative force, which could reshape the realm of healthcare by revolutionising medical education, patient care and translational research.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e AI tools have been developed in healthcare that have the capability to enhance diagnostic precision, decision-making, prognostication, and management of patients. AI has been integrated into routine healthcare for patients’ recordkeeping, drug discovery, therapy selection, surgical planning and surgical support, such as in robotic surgery. AI has already become an integral part of radiodiagnosis, reinforcing diagnostic accuracy and streamlining workflow.\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDespite the previous publications, the optimal use of AI in medicine is not well known. Also, healthcare workers are in general anxious and have concerns about the effectiveness of AI, mainly because of a lack of awareness, knowledge, and education among medical practitioners. To overcome this, the medical curriculum requires an overhaul which would improve the willingness to use AI effectively in future. The current study aimed to assess the awareness, perceptions, and opinions regarding AI among medical students and resident doctors at a tertiary care teaching hospital in North India.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy design, Setting, and Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003e We conducted a questionnaire-based, cross-sectional survey among medical students and resident doctors at a tertiary care teaching hospital in North India, from 20.3.25 to 29.4.25. All MBBS students from the first year to the final year were invited to participate along with all resident doctors. The study protocol was ethically approved by the Institutional Ethics Committee [AIIMSA3369/11.03.2025]. Participation in this study was voluntary and not compensated. Electronic informed consent was obtained from all the participants. Medical students and resident doctors who did not give consent were excluded from the study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDevelopment and administration of the questionnaire\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe first section of the questionnaire collected demographic information of the participants, including their age, gender, year of study, and department (for postgraduate students). Subsequent sections collected data on their knowledge and attitudes towards AI for which a total of sixteen questions were used to assess. The questionnaire used in the present study was developed for this study only and content validity of the questionnaire was adequately assessed (Supplementary PDF file attached of the questionnaire used in the present study). All the participants were informed about the confidential nature of the collected data and its use for the purposes of this study only.\u003c/p\u003e\u003cp\u003eThe initial questionnaire underwent content validation by an independent consultant, who also assessed the average response time and consistency of the questionnaire. Subsequently, the questionnaire was transformed into an online questionnaire (Google Forms, Google Inc.), and participants were provided with the link to respond.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eR version 4.3.2 (R Studio) was used for statistical analysis. Descriptive statistics were used to represent proportions, means, and standard deviations (SD), or medians and interquartile ranges (IQR). For between-group comparisons of continuous or ordinal variables, the Kruskal-Wallis rank sum test and two-way ANOVA were used. For comparisons of categorical variables, Pearson’s Chi-square test was used.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 194 participants successfully submitted the questionnaire, including 113 (58.25%) medical students and 81 (41.75%) resident postgraduate doctors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e: Among the 194 participants, the ages ranged from 19 to 32 years, with a mean age of 23.91 years. The study participants were predominantly male (78.35%) with a male-to-female ratio of 3.62:1. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the baseline characteristics of the study participants.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the study participants [n\u0026thinsp;=\u0026thinsp;194]\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal [N\u0026thinsp;=\u0026thinsp;194]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedical students [n\u0026thinsp;=\u0026thinsp;113]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResident doctors [N\u0026thinsp;=\u0026thinsp;81]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge range in years (Mean age)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 to 32 (23.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 to 23 (21.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 to 32 (26.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (21.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (19.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (24.70%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e152 (78.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91 (80.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61 (75.30%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e63.41% of the participants declared their knowledge to be poor to below average, and another 24.74% said that they had average knowledge about AI. Only 11.85% of the study participants said that they had above-average or excellent knowledge of AI (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This distribution did not vary significantly when compared amongst medical students and postgraduate resident doctors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 115 (59.28%) participants felt that AI tools will be effective in making their understanding of medical concepts. Similar trends were seen when the answers of medical students and resident doctors were compared (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDepicts how the participants (Medical students and Resident doctors] rated themselves for their knowledge and effectiveness of AI tools used in healthcare\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuestions asked on Artificial intelligence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lsquo;Poor\u0026rsquo; n (%age)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lsquo;Below average\u0026rsquo; n (%age)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lsquo;Average\u0026rsquo; n (%age)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lsquo;Above average\u0026rsquo; n (%age)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lsquo;Excellent\u0026rsquo; n (%age)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHow is your knowledge about AI/ machine learning algorithms? [Total respondents\u0026thinsp;=\u0026thinsp;194]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73 (37.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50 (25.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48 (24.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21 (10.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2 (1.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHow will you rate the effectiveness of AI tools in enhancing your understanding of medical concepts? [Total respondents\u0026thinsp;=\u0026thinsp;115]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47 (40.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34 (29.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8 (6.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen asked about the exposure of AI in their medical curriculum, 84.62% of participants denied the exposure of any topics related to AI in their medical curriculum, and another 8.66% of participants responded as \u0026lsquo;not sure\u0026rsquo;. Only 6.71% of participants responded as \u0026lsquo;yes\u0026rsquo; for the exposure of AI in their medical curriculum. Similar results were observed when they were asked about whether AI is being used as a teaching method in the medical curriculum, with 85.05% of participants responding as \u0026lsquo;no\u0026rsquo;, 11.85% as \u0026lsquo;not sure\u0026rsquo; and 3.09% as \u0026lsquo;yes\u0026rsquo; (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When asked questions like, \u0026ldquo;Are you familiar with the ethical considerations associated with the use of AI in healthcare, particularly concerning patient privacy and data security?\u0026rdquo;- the response of \u0026ldquo;no/not sure\u0026rdquo; was given by 81.44% of participants, and \u0026ldquo;Would you be interested in further education and training programs focused on AI in healthcare?\u0026rdquo; - the response as \u0026ldquo;yes\u0026rdquo; was given by 83.5% of participants, with the responses of medical students as well as resident doctors being similar (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e compiles the various AI tools used by medical students and resident doctors, and it was seen that ChatGPT was the most frequently used AI tool amongst all the participants.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics for knowledge and attitudes towards artificial intelligence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuestions asked on Artificial intelligence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;194)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lsquo;No\u0026rsquo;\u003c/p\u003e\u003cp\u003en (%age)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lsquo;Not sure\u0026rsquo; n (%age)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lsquo;Yes\u0026rsquo;\u003c/p\u003e\u003cp\u003en (%age)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHave you been introduced to AI topics in your medical curriculum?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedical students\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95 (84.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (12.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4 (3.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResident doctors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69 (85.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4 (4.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8 (9.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIs AI being used as a teaching method in your curriculum?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedical students\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95 (84.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (12.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4 (3.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResident doctors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70 (86.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9 (11.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (2.47)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAre you familiar with the ethical considerations associated with the use of AI in healthcare, particularly concerning patient privacy and data security?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedical students\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19 (16.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75 (66.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19 (16.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResident doctors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58 (71.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17 (20.99)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWould you be interested in further education and training programs focused on AI in healthcare?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedical students\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9 (7.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e98 (86.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResident doctors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (11.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8 (9.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e64 (79.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eParticipants primarily used AI tools for tasks like correcting language (97/194; 50%), conducting literature reviews, statistical analysis and manuscript writing (84/194; 43.3%), creating presentation outlines (72/194; 37.11%), taking notes from detailed content (53/194; 27.32%), and aiding in documentation like converting image or spoken words into text (42/194; 21.65%). Fewer participants engaged in miscellaneous activities like accessing classified documents, coding in Python, general information, writing letters to faculty, drug interactions and making multiple-choice questions (MCQs).\u003c/p\u003e\u003cp\u003eWhen they were asked about their understanding of AI, 107/194; 55.15% of participants were unfamiliar with AI terminologies, while the remaining participants showed awareness of concepts such as machine learning (67/194; 34.54%), neural networks (39/194; 20.1%), generative versus predictive AI (35/194; 18.04%), augmented intelligence (32/194; 16.49%), large language models [LLM] (32/194; 16.49%), and natural language processing (23/194; 11.85%).\u003c/p\u003e\u003cp\u003eWhen enquired about the awareness of the role of AI in diagnostics, 71/194; 36.6% denied any kind of awareness, while 112/194; 57.73% of participants knew that AI can provide differential diagnoses by integrating multiple datasets. Other less frequent roles mentioned included AI tools for analysing chest X-rays (66; 34%), for classification of skin lesions (57; 29.38%), for histopathological assessment (49; 25.26%), for identifying diabetic retinopathy (28; 14.43%) and for the detection of stroke (14; 7.22%) and heart failure (12; 6.18%).\u003c/p\u003e\u003cp\u003eSimilarly, when enquired about the knowledge of AI application in therapeutics, 97/194; 50% of participants denied any knowledge, while 69; 35.57% of participants said that AI can provide more personalised treatment by integrating multiple data points. Others mentioned that AI tools are used to increase drug adherence (42; 21.65%), AI is used in targeted drug development (39; 20.1%), AI is being used in CRISPR technology (36; 18.56%), and AI tools are used in the NHS to reduce waiting time by segmentation in cancer patients\u0026rsquo; radiotherapy (12; 6.18%).\u003c/p\u003e\u003cp\u003eRegarding awareness of mobile applications used in the patient care, 53/194; 27.32% denied any kind of awareness, however majority of the participants (118; 60.82%) were aware of IOS Health or Samsung Health, which can store health information, offer medication, track calories, sleep duration and integrate data from various devices. 97/194; 50% of participants were also aware of these mobile applications used by diabetics in order to maintain a glucose log track, carbohydrate intake and exercise, send insulin reminders, facilitate communication with doctors and determine insulin doses, etc. 20; 10.31% of participants were aware of virtual health assistants or apps, and 14; 7.22% of participants were aware of clinical decision support systems.\u003c/p\u003e\u003cp\u003eIn terms of awareness of AI-enabled devices, 36/194, or 18.56%, of participants were unaware of any such devices. Around 148; 76.29% were familiar with smartwatches like the Apple Watch, which are capable of tracking physical activities, monitoring workouts, measuring heart rate, conducting ECGs and detecting blood oxygen fall, etc. 104/194; 53.61% of the participants were aware of the X3M Littman CORE Digital Stethoscope, which can be used to analyse murmurs. Only 28; 14.43% and 17/194; 8.76% were aware of AI-assisted ultrasound tools that can be used to enhance the accuracy of point-of-care ultrasound and AI-enabled mechanical ventilators for automated weaning, respectively. Lastly, knowledge of sensors for remote monitoring of sleep, breathing, and behaviour was present in 14; 7.22% of the participants.\u003c/p\u003e\u003cp\u003eRegarding ethical considerations in the use of AI in medical practices, 158/194; 81.44% showed unawareness of such issues. When asked about the strategies to improve the handling of patients\u0026rsquo; data, 90 participants (46.39%) emphasized anonymizing data sharing for research, and 99 participants (51.03%) highlighted the importance of obtaining informed consent from patients for the utilization of their data in AI applications. Another 76; 39.18% of participants advised for implementing regular audits and real-time monitoring systems to prevent unauthorized access, 68; 35.05% supported the development of algorithms with transparent decision-making processes, and 58; 29.9% emphasized collecting and storing only essential patient data for AI purposes.\u003c/p\u003e\u003cp\u003eParticipants were asked about their views on methods to motivate medical professionals to engage in AI-driven healthcare advancements. The majority (158/194; 81.44%) advocated for familiarizing medical professionals with AI technologies and highlighting their benefits in enhancing patient care, reducing workload, and improving efficiency. Additionally, 132; 68.04% suggested collaboration with other national institutions such as the Indian Institute of Technology (IIT), while 76; 39.18% recommended incentives and recognition. Another 73; 37.63% proposed facilitating networking, and 66; 34.02% stressed addressing privacy and security concerns. Conversely, 17; 8.76% indicated having no opinion on the matter.\u003c/p\u003e\u003cp\u003eParticipants also opined about various barriers in incorporating AI in healthcare, including data privacy and security concerns (106; 54.64%), doubts about reliability and accuracy of AI algorithms (109; 56.19%), limited AI expertise (125; 64.43%), financial constraints (55; 28.35%), lack of standardized protocols for AI applications (111; 57.22%), resistance to change among professionals (94; 48.45%), strict healthcare industry regulations (54; 27.84%), and challenges in decision-making authority alignment (1; 0.52%). Additionally, inadequate data storage systems pose a hurdle to accurate AI utilization (1; 0.52%). These hurdles highlight the multifaceted challenges facing the integration of AI in healthcare.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study was a survey-based cross-sectional study of 113 medical students and 81 resident doctors from a tertiary healthcare centre in North India. The majority of the medical students and resident doctors reported low-level knowledge and limited understanding of AI, underscoring a dire need to learn about the various applications of AI in medical practice and its legal and ethical implications, especially in the present era, marked by the ever-expanding role of AI in medical practice. We did not find significant differences between medical students and resident doctors in their perception about the utility of various AI tools, and this finding was consistent with other previous reports that trainees in the medical field had little exposure to AI in their medical curriculum. A recent web-based survey compared AI-related attitudes amongst 328 medical students and 66 pathology trainees and highlighted that both the groups had similar attitudes about AI knowledge and the majority learnt about the applications of AI through various websites, and this was not part of their medical curriculum.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Another recent questionnaire-based cross-sectional study conducted in Riyadh, Saudi Arabia, included 58 resident doctors, 150 medical students and 166 interns. Most of the participants in this study reported having knowledge of AI and about half were aware of its applications in medical practice as well as various AI subtypes such as deep learning and machine learning. About 40% of respondents were taught AI in their medical school, and nearly three-fourths of all participants expressed a strong need for the inclusion of AI in the medical curriculum.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e The present study and the previous studies suggest a significant knowledge gap in the familiarity and accessibility of structured and well-defined AI curriculum frameworks designed specifically for medical education and training.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Although the medical students and practitioners are aware of AI\u0026rsquo;s potential for early diagnosis and disease management, their concerns regarding ethical and legal implications, the possibility of potential errors and fears of being replaced by AI are valid. Wang F et al. provide a comprehensive review of AI applications in healthcare along with challenges and practical implications of AI in using multiple datasets, research and developmental data and behavioural and wellness data.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Overall, there was a strong intent to have quality education and sound knowledge of AI amongst medical students and resident doctors.\u003c/p\u003e\u003cp\u003eAn Indian study from Kanpur, published in the year 2021, collected responses from 401 medical students across two medical colleges. More than 80% of the participants believed that education in AI would be beneficial for their future medical practice and agreed that it should be included in the medical curriculum.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e This echoes with another Iranian report where over 96% of medical students demanded enhanced knowledge and skills in AI, indicating that trainees across regions recognize the relevance of AI and support its formal integration into medical education.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e The present study is in sync with the previously published studies highlighting a lack of adequate knowledge about AI tools in medical practice and a strong urge to learn this technology amongst doctors and medical students.\u003c/p\u003e\u003cp\u003eMedical students and doctors have reported use of AI tools, with ChatGPT being the most frequently used AI tool, especially for language and grammatical checks, literature reviews, statistical analysis, manuscript writing, creating presentation outlines and preparing notes. Few participants also used AI for accessing classified documents, coding in Python, drug interactions and preparing MCQs. A U.S.-based survey also highlighted similar findings, indicating that ChatGPT was used by doctors to write, revise and summarize text.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Additionally, other systematic reviews also highlighted the utility of ChatGPT in literature searches, drafting reports, and preparing concise summaries.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Large language models (LLMs) have been used by clinicians for a variety of tasks such as translating and correcting medical text, drafting patient instructions, assisting in diagnostic report generation, and understanding the interpretation of complex differential diagnoses in challenging clinical scenarios.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e We, in the present study, also had similar findings, which strongly implicate that the application of AI tools is already being done informally in medical practice.\u003c/p\u003e\u003cp\u003eAwareness about AI-related terminologies and applications of AI in diagnostics and therapeutics, along with mobile applications and AI-enabled devices used in medical practice, remains inconsistent amongst medical students and resident doctors. In the present study, although a subset of respondents demonstrated familiarity with terms such as \u0026lsquo;neural network\u0026rsquo;, \u0026lsquo;augmented intelligence\u0026rsquo; and \u0026lsquo;machine learning\u0026rsquo;, a significant proportion lacked foundational understanding of basic AI concepts. Similar disparities were highlighted in previous studies, where, despite growing interest in AI, familiarity with AI subtypes and operational mechanisms was limited.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e AI-assisted diagnostic tools are more frequently being used in the fields of pathology, dermatology and radiodiagnosis, with medical practitioners having limited awareness regarding specific applications such as histopathological classification and staging and automated image interpretation.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Although participants reported using mobile health applications (e.g., glucose monitoring apps, fitness trackers, etc.), fewer were aware of advanced AI-enabled devices like digital stethoscopes, point-of-care ultrasound systems, automated ventilators, CRISPR technology, sleep monitors, etc. This heterogeneity in knowledge indicates a lack of standardized exposure and underscores the need for structured, curriculum-based education to ensure uniform competency in emerging AI technologies across all levels of medical training.\u003c/p\u003e\u003cp\u003eOur participants had ethical and practical concerns, especially regarding data privacy, AI related biases and potential reductions in clinical skills due to the easy availability of AI tools and services.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e A previous survey from Riyadh, Saudi Arabia, found that over three-quarters of medical trainees expressed concerns regarding the potential impact of AI on job security.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Additionally, previous studies have shown a declining interest in pursuing radiodiagnosis as a speciality, likely influenced by the rapid transformation in this field due to increasing integration of AI.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e In our survey also, respondents have advocated for strict guidelines and education on AI\u0026rsquo;s limitations and ethical/legal implications, which is in line with global calls for \u0026ldquo;AI in the loop\u0026rdquo; oversight.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Some actionable improvements have also been suggested, such as case discussion covering AI\u0026rsquo;s limitations and drawbacks.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e For instance, in a study from Kanpur, India, medical students emphasized that the medical curriculum should cover applications, strengths and limitations of AI and participants of the present study also echoed this by raising a need for training in ethical and legal aspects of AI and various regulations related to it.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eOur findings accord with the broader English literature. Recent surveys worldwide report similarly low AI literacy coupled with positive attitudes toward its potential and a strong urge to learn its applications and limitations. Another 26-question survey with 702 medical students as respondents from Pakistan highlighted that medical students viewed AI as an effective learning aid and endorsed its formal integration into medical education, as it was better than traditional tools such as books and lectures.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Another Indian study highlighted the importance of incorporating AI-related coursework into medical curricula to prepare future healthcare professionals.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Previous study from Portugal and a study by Civaner et al. evaluated the integration of AI into medical education and highlighted the importance of incorporating AI-related competencies into medical curricula.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn studies from Riyadh, Saudi Arabia, and Iran, medical trainees recognized the importance of AI in diagnosis yet identified a stark lack of formal training.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Previous reviews also have noted that to date no medical schools mandate AI courses, though some institutions (e.g., Duke University) have begun offering AI modules.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e These parallels underscore a global consensus: while young clinicians appreciate AI\u0026rsquo;s promise, medical education has not yet caught up with technological advances.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Our observations in North India fit this pattern. The results of the present study are in sync with previously published research which highlights a strong interest amongst medical trainees to learn about AI tools, coupled with mention of knowledge gaps highlighting an urgent need for structured AI education. Studies have recommended the introduction of AI literacy, including foundational concepts in data science and machine learning, during the early years of medical training.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Pilot studies have been conducted for effective implementation of practical courses with hands-on projects to link AI tools and concepts with clinical scenarios.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Approximately 96% of medical students and resident doctors in our study expressed a desire for formal training in AI, further reinforcing the necessity of incorporating AI education in medical curricula and training.\u003c/p\u003e\u003cp\u003eThe present study has a few limitations and drawbacks. Data was collected using a self-report questionnaire at a single teaching hospital, so responses may reflect local context and personal perceptions rather than objective skill. As in other surveys, the convenience sample and survey-based design could introduce response bias. We did not assess actual AI knowledge or performance (only participants\u0026rsquo; confidence and attitudes), nor did we measure prior exposure to AI or language proficiency, which may affect familiarity. These factors limit generalizability beyond similar settings. Nonetheless, the consistency of our findings with those from diverse regions suggests these patterns - low AI literacy but high interest - are broadly representative of current medical training environments.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present study, including medical students and resident doctors from a tertiary healthcare centre, reveals a striking dichotomy with limited knowledge of AI despite enthusiastic demand for education in AI. Both students and residents recognize AI\u0026rsquo;s growing role yet report minimal exposure in their medical curriculum. The participants commonly used AI tools for writing and information retrieval but also expressed valid ethical and legal concerns. These insights align with global reports and reinforce the call for curricula reform. Our medical curriculum should incorporate formal AI training, covering technical fundamentals, clinical applications, and ethical implications in a structured way in order to prepare clinicians for an AI-enriched future. Such initiatives will ensure that emerging physicians are equipped to harness AI safely and effectively in patient care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArtificial intelligence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRISPR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eClustered regularly interspaced short palindromic repeats\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterquartile ranges\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIIT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIndian Institute of Technology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLLM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLarge language models\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMCQS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMultiple-choice questions\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard deviations\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThe study protocol was ethically approved by the Institutional Ethics Committee [AIIMSA3369/11.03.2025] by Institute Ethics Committee, AIIMS, New Delhi. As the present research study was carried out on humans and/or human data, we adhered to the Declaration of Helsinki in our study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate and Publish:\u003c/strong\u003e Consent to participate and publish was obtained from every participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest’s statement:\u0026nbsp;\u003c/strong\u003eNothing to disclose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial and non-financial competing interests and Funding:\u0026nbsp;\u003c/strong\u003eNothing to disclose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIG:\u0026nbsp;\u003c/strong\u003eConceptualization, Data acquisition, Analysis and interpretation, Literature review, Writing the initial draft and revision, Approval of the final version of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAG:\u0026nbsp;\u003c/strong\u003eData analysis, Writing the initial draft and revision, Literature review, Approval of the final version of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAV:\u0026nbsp;\u003c/strong\u003eFormal analysis, Review, Editing, Approval of the final version of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSJ:\u003c/strong\u003e Data interpretation, Supervision, Clinical patient management, Manuscript editing, Approval of the final version of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNW:\u0026nbsp;\u003c/strong\u003eData interpretation, Supervision, Clinical patient management, Manuscript editing, Approval of the final version of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements: Nil\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information (optional): NA\u003c/strong\u003e\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAronson JK. Artificial intelligence in pharmacovigilance: An introduction to terms, concepts, applications, and limitations. Drug Saf. 2022;45:407\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScheetz J, Rothschild P, McGuinness M, Hadoux X, Soyer HP, Janda M, et al. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep. 2021;11:5193.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTran AQ, Nguyen LH, Nguyen HSA, Nguyen CT, Vu LG, Zhang M, et al. Determinants of intention to use artificial intelligence-based diagnosis support system among prospective physicians. Front Public Health. 2021;9:755644.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNajjar R. Redefining radiology: A review of artificial intelligence integration in medical imaging. Diagnostics. 2023;13(17):2760.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHardy M, Harvey H. Artificial intelligence in diagnostic imaging: impact on the radiography profession. Br J Radiol. 2020;93:20190840.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarreiro-Ares A, Morales-Santiago A, Sendra-Portero F, Souto-Bayarri M. Impact of the rise of artificial intelligence in radiology: What do students think? Int J Environ Res Public Health. 2023;20:1589.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHakami KM, Alameer M, Jaawna E, Sudi A, Bahkali B, Mohammed A, Hakami A, Mahfouz MS, Alhazmi AH, Dhayihi TM. The impact of artificial intelligence on the preference of radiology as a future specialty among medical students at Jazan University, Saudi Arabia: A cross-sectional study. Cureus. 2023;15:e41840.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRjoop A, Al-Qudah M, Alkhasawneh R, Bataineh N, Abdaljaleel M, Rjoub MA, et al. Awareness and attitude toward artificial intelligence among medical students and pathology trainees: Survey study. JMIR Med Educ. 2025;11:e62669.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlabbad FA, Almeneessier AS, Alshalan MH, Aljarba MN. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Saudi Arabia. J Family Med Prim Care. 2025;14:1459\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTolentino R, Baradaran A, Gore G, Pluye P, Abbasgholizadeh-Rahimi S. Curriculum frameworks and educational programs in AI for medical students, residents, and practicing physicians: Scoping review. JMIR Med Educ. 2024;10:e54793.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang F, Preininger A. AI in health: State of the art, challenges, and future directions. Yearb Med Inf. 2019;28:16\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSachdev R, Garg K, Srivastava A. Awareness and education of medical students toward artificial intelligence and radiology: A cross-sectional multicenter survey at Kanpur, Uttar Pradesh. Dentistry Med Res. 2021;9:77\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRezazadeh H, Mahani AM, Salajegheh M. Insights into the future: Assessing medical students' artificial intelligence readiness- A cross-sectional study at Kerman University of Medical Sciences (2022). Health Sci Rep. 2025;8:e70870.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang JS, Yoon C, Williams DKA, Pinkas A. Exploring the usage of ChatGPT among medical students in the United States. J Med Educ Curric Dev. 2024;11:23821205241264695.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSallam M. 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Medical students\u0026rsquo; attitudes toward AI in education: perception, effectiveness, and its credibility. BMC Med Educ. 2025;25:82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJindal A, Bansal M. Knowledge and education about artificial intelligence among medical students from teaching institutions of India: A brief survey. MedEdPublish. 2020;9:200.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePedro AR, Dias MB, Laranjo L, Cunha AS, Cordeiro JV. Artificial intelligence in medicine: A comprehensive survey of medical doctor\u0026rsquo;s perspectives in Portugal. PLoS ONE. 2023;18:e0290613.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCivaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ. 2022;22:772.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial intelligence, Attitude, ChatGPT, Knowledge, Machine learning, Medical curriculum, Medical students, Resident doctors","lastPublishedDoi":"10.21203/rs.3.rs-7059359/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7059359/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cu\u003e\u003cem\u003eBackground:\u003c/em\u003e\u003c/u\u003e Artificial intelligence (AI) holds promise in reshaping healthcare by transforming educational patterns, patient care, and research opportunities. However, there are obstacles impeding the proper integration of AI into the medical field. This study was undertakento evaluate the knowledge, attitude, and awareness of medical students and resident doctors regarding AI in medicine and healthcare.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cem\u003eMethods:\u003c/em\u003e\u003c/u\u003eA questionnaire-based survey was conducted that included a total of 16 questions specifically designed to assess the knowledge and attitude of participants towards AI. The questionnaire used in the present study was developed for this study only and content validity of the initial questionnaire was adequately assessed. The questionnaire was converted into a Google Form, and participants were provided with the link to complete it. Statistical analysis was conducted using R version 4.3.2 (R-Studio).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cem\u003eResults:\u003c/em\u003e\u003c/u\u003e\u003cem\u003e \u003c/em\u003eOut of 194 respondents, 113 (58.25%) were medical students, and 81 (41.75%) were resident postgraduate doctors aged 19 to 32 (average 23.91 years) and a male-to-female ratio of 3.62:1. While 63.41% rated their AI knowledge as poor to below average, with 55.15% lacking understanding of many AI terminologies, 59.28% believed AI tools could enhance their understanding of medical concepts. 83.5% expressed interest in furthering knowledge on AI in healthcare. ChatGPT was the most used AI tool, primarily for language correction (50%), literature reviews and\u003cstrong\u003e \u003c/strong\u003emanuscript writing (43.3%), and creating presentation outlines (37.11%). Additionally, knowledge about AI devices and apps applicable to diagnostics, therapeutics, patient care, and data analysis was evaluated, along with opinions on barriers to incorporating AI in healthcare. 81.44% of respondents were unaware of AI's ethical considerations.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cem\u003eConclusions:\u003c/em\u003e\u003c/u\u003e AI has immense potential across diverse healthcare sectors. Nonetheless, our study also underscores the pressing need to confront challenges and equip our future healthcare professionals with the evolving realm of AI. This is essential to ensure they can effectively apply practical AI knowledge for enhanced patient care and management.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence in Medicine: A Cross-sectional Study of Knowledge and Attitudes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 05:40:14","doi":"10.21203/rs.3.rs-7059359/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T06:21:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-30T17:10:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267895017620465031925843754832269941775","date":"2025-12-21T08:38:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-18T19:32:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276829479910656313072136964052840799290","date":"2025-12-18T17:40:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-05T08:10:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168508624989303568007034486078448395498","date":"2025-07-31T06:25:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-15T17:25:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T11:11:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-14T13:19:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-07-14T13:16:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4f0b7c1-4500-45c1-af9d-db912997920f","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:27:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 05:40:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7059359","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7059359","identity":"rs-7059359","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}